Lin Zhang


PCBERT: Parent and Child BERT for Chinese Few-shot NER
Peichao Lai | Feiyang Ye | Lin Zhang | Zhiwei Chen | Yanggeng Fu | Yingjie Wu | Yilei Wang
Proceedings of the 29th International Conference on Computational Linguistics

Achieving good performance on few-shot or zero-shot datasets has been a long-term challenge for NER. The conventional semantic transfer approaches on NER will decrease model performance when the semantic distribution is quite different, especially in Chinese few-shot NER. Recently, prompt-tuning has been thoroughly considered for low-resource tasks. But there is no effective prompt-tuning approach for Chinese few-shot NER. In this work, we propose a prompt-based Parent and Child BERT (PCBERT) for Chinese few-shot NER. To train an annotating model on high-resource datasets and then discover more implicit labels on low-resource datasets. We further design a label extension strategy to achieve label transferring from high-resource datasets. We evaluated our model on Weibo and the other three sampling Chinese NER datasets, and the experimental result demonstrates our approach’s effectiveness in few-shot learning.

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
Ping Yang | Junjie Wang | Ruyi Gan | Xinyu Zhu | Lin Zhang | Ziwei Wu | Xinyu Gao | Jiaxing Zhang | Tetsuya Sakai
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis. Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. It not only adds generalization ability to models but also significantly reduces the number of parameters. Our method shares the merits of efficient training and deployment. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification. Our model achieves this success with only 235M parameters, which is substantially smaller than state-of-the-art models with billions of parameters. The code and pre-trained models are available at .